Patch-aware Batch Normalization for Improving Cross-domain Robustness
- URL: http://arxiv.org/abs/2304.02848v3
- Date: Sat, 14 Sep 2024 10:29:28 GMT
- Title: Patch-aware Batch Normalization for Improving Cross-domain Robustness
- Authors: Lei Qi, Dongjia Zhao, Yinghuan Shi, Xin Geng,
- Abstract summary: Cross-domain tasks present a challenge in which the model's performance will degrade when the training set and the test set follow different distributions.
We propose a novel method called patch-aware batch normalization (PBN)
By exploiting the differences between local patches of an image, our proposed PBN can effectively enhance the robustness of the model's parameters.
- Score: 55.06956781674986
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Despite the significant success of deep learning in computer vision tasks, cross-domain tasks still present a challenge in which the model's performance will degrade when the training set and the test set follow different distributions. Most existing methods employ adversarial learning or instance normalization for achieving data augmentation to solve this task. In contrast, considering that the batch normalization (BN) layer may not be robust for unseen domains and there exist the differences between local patches of an image, we propose a novel method called patch-aware batch normalization (PBN). To be specific, we first split feature maps of a batch into non-overlapping patches along the spatial dimension, and then independently normalize each patch to jointly optimize the shared BN parameter at each iteration. By exploiting the differences between local patches of an image, our proposed PBN can effectively enhance the robustness of the model's parameters. Besides, considering the statistics from each patch may be inaccurate due to their smaller size compared to the global feature maps, we incorporate the globally accumulated statistics with the statistics from each batch to obtain the final statistics for normalizing each patch. Since the proposed PBN can replace the typical BN, it can be integrated into most existing state-of-the-art methods. Extensive experiments and analysis demonstrate the effectiveness of our PBN in multiple computer vision tasks, including classification, object detection, instance retrieval, and semantic segmentation.
Related papers
- Supervised Batch Normalization [0.08192907805418585]
Batch Normalization (BN) is a widely-used technique in neural networks.
We propose Supervised Batch Normalization (SBN), a pioneering approach.
We define contexts as modes, categorizing data with similar characteristics.
arXiv Detail & Related papers (2024-05-27T10:30:21Z) - Unified Batch Normalization: Identifying and Alleviating the Feature
Condensation in Batch Normalization and a Unified Framework [55.22949690864962]
Batch Normalization (BN) has become an essential technique in contemporary neural network design.
We propose a two-stage unified framework called Unified Batch Normalization (UBN)
UBN significantly enhances performance across different visual backbones and different vision tasks.
arXiv Detail & Related papers (2023-11-27T16:41:31Z) - Generalizable Person Re-Identification via Self-Supervised Batch Norm
Test-Time Adaption [63.7424680360004]
Batch Norm Test-time Adaption (BNTA) is a novel re-id framework that applies the self-supervised strategy to update BN parameters adaptively.
BNTA explores the domain-aware information within unlabeled target data before inference, and accordingly modulates the feature distribution normalized by BN to adapt to the target domain.
arXiv Detail & Related papers (2022-03-01T18:46:32Z) - Test-time Batch Statistics Calibration for Covariate Shift [66.7044675981449]
We propose to adapt the deep models to the novel environment during inference.
We present a general formulation $alpha$-BN to calibrate the batch statistics.
We also present a novel loss function to form a unified test time adaptation framework Core.
arXiv Detail & Related papers (2021-10-06T08:45:03Z) - Separable Batch Normalization for Robust Facial Landmark Localization
with Cross-protocol Network Training [41.82379935715916]
A big, diverse and balanced training data is the key to the success of deep neural network training.
A small dataset without diverse and balanced training samples cannot support the training of a deep network effectively.
This paper presents a novel Separable Batch Normalization (SepBN) module with a Cross-protocol Network Training (CNT) strategy for robust facial landmark localization.
arXiv Detail & Related papers (2021-01-17T13:04:06Z) - Cross-Iteration Batch Normalization [67.83430009388678]
We present Cross-It Batch Normalization (CBN), in which examples from multiple recent iterations are jointly utilized to enhance estimation quality.
CBN is found to outperform the original batch normalization and a direct calculation of statistics over previous iterations without the proposed compensation technique.
arXiv Detail & Related papers (2020-02-13T18:52:57Z) - Towards Stabilizing Batch Statistics in Backward Propagation of Batch
Normalization [126.6252371899064]
Moving Average Batch Normalization (MABN) is a novel normalization method.
We show that MABN can completely restore the performance of vanilla BN in small batch cases.
Our experiments demonstrate the effectiveness of MABN in multiple computer vision tasks including ImageNet and COCO.
arXiv Detail & Related papers (2020-01-19T14:41:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.